Robert Kirk Delisle

Discovery of a Novel Class of Imidazo[1,2-a]Pyridines with Potent PDGFR Activity and Oral Bioavailability.

The in silico construction of a PDGFRβ kinase homology model and ensuing medicinal chemistry guided by molecular modeling, led to the identification of potent, small molecule inhibitors of PDGFR. Subsequent exploration of structure-activity relationships (SAR) led to the incorporation of a constrained secondary amine to enhance selectivity. Further refinements led to the integration of a fluorine substituted piperidine, which resulted in significant reduction of P-glycoprotein (Pgp) mediated efflux and improved bioavailability. Compound 28 displayed oral exposure in rodents and had a pronounced effect in a pharmacokinetic-pharmacodynamic (PKPD) assay.

Technique for Generating Three-Dimensional Alignments of Multiple Ligands from One-Dimensional Alignments

We describe and demonstrate a method for the simultaneous, fully flexible alignment of multiple molecules with a common biological activity. The key aspect of the algorithm is that the alignment problem is first solved in a lower dimensional space, in this case using the one-dimensional representations of the molecules. The three-dimensional alignment is then guided by constraints derived from the one-dimensional alignment. We demonstrate using 10 hERG channel blockers, with a total of 72 rotatable bonds, that the one-dimensional alignment is able to effectively isolate key conserved pharmacophoric features and that these conserved features can effectively guide the three-dimensional alignment. Further using 10 estrogen receptor agonists and 5 estrogen receptor antagonists with publicly available cocrystal structures we show that the method is able to produce superpositions comparable to those derived from crystal structures. Finally, we demonstrate, using examples from peptidic CXCR3 agonists, that the method is able to generate reasonable binding hypotheses.

Computational ADME/Tox Modeling: Aiding Understanding and Enhancing Decision Making in Drug Design

With recent estimates of drug development costs on the order of

Improving the performance of self-organizing maps via growing representations

800 million and increased pressure to reduce consumer drug costs, it is not surprising that the pharmaceutical industry is keenly interested in reducing the overall expense associated with drug development. An analysis of the reasons for attrition during the drug development process found that over half of all failures can be attributed to problems with human or animal pharmacokinetics and toxicity. Discovering pharmacokinetics and toxicity liabilities late within the drug development process results in wasted resource expenditures. This argues dramatically for evaluation of these properties as early as possible, leading to the concept of Fail Early. Computational models provide a low cost, flexible evaluation of compound properties that can be implemented and used prior to chemical synthesis thereby creating an alternative philosophy of Design for Success. Here we review the history and current trends within ADME/Tox modeling and discuss important issues related to development of computational models. In addition, we review some of the commercially available tools to achieve this goal as well as methods developed internally to address these issues from the design stage through development and optimization of drug candidates. In particular, we highlight those features that we feel best exemplify the Design for Success philosophy.

Malarial Kinases: Novel Targets for In Silico Approaches to Drug Discovery

Self-organizing maps (SOMs) are a type of artificial neural network that through training can produce simplified representations of large, high dimensional data sets. These representations are typically used for visualization, classification, and clustering and have been successfully applied to a variety of problems in the pharmaceutical and bioinformatics domains. SOMs in these domains have generally been restricted to static sets of nodes connected in either a grid or hexagonal connectivity and planar or toroidal topologies. We investigate the impact of connectivity and topology on SOM performance, and experiments were performed on fixed and growing SOMs. Three synthetic and two relevant data sets from the chemistry domain were used for evaluation, and performance was assessed on the basis of topological and quantization errors after equivalent training periods. Although we found that all SOMs were roughly comparable at quantizing a data space, there was wide variation in the ability to capture its underlying structure, and growing SOMs consistently outperformed their static counterparts in regards to topological errors. Additionally, one growing SOM, the Neural Gas, was found to be far more capable of capturing details of a target data space, finding lower dimensional relationships hidden within higher dimensional representations.

Benzylic Oxidation of Gemfibrozil-1-O-β-Glucuronide by P450 2C8 Leads to Heme Alkylation and Irreversible Inhibition

Malaria, the disease caused by infection with protozoan parasites from the genus Plasmodium, claims the lives of nearly 1 million people annually. Developing nations, particularly in the African Region, bear the brunt of this malaria burden. Alarmingly, the most dangerous etiologic agent of malaria, Plasmodium falciparum, is becoming increasingly resistant to current first-line antimalarials. In light of the widespread devastation caused by malaria, the emergence of drug-resistant P. falciparum strains, and the projected decrease in funding for malaria eradication that may occur over the next decade, the identification of promising new targets for antimalarial drug design is imperative. P. falciparum kinases have been proposed as ideal drug targets for antimalarial drug design because they mediate critical cellular processes within the parasite and are, in many cases, structurally and mechanistically divergent when compared with kinases from humans. Identifying a molecule capable of inhibiting the activity of a target enzyme is generally an arduous and expensive process that can be greatly aided by utilizing in silico drug design techniques. Such methods have been extensively applied to human kinases, but as yet have not been fully exploited for the exploration and characterization of antimalarial kinase targets. This review focuses on in silico methods that have been used for the evaluation of potential antimalarials and the Plasmodium kinases that could be explored using these techniques.